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On the complexity of computing Markov perfect equilibrium in general-sum stochastic games

National science Review 2023年第1期10卷 288-301页

作者： Xiaotie Deng Ningyuan Li David Mguni Jun Wang Yaodong Yang Center on Frontiers of Computing Studies School of Computer Science Peking University Center for Multi-Agent Research Institute for AI Peking University Huawei UK Computer Science University College London

Similar to the role of Markov decision processes in reinforcement learning,Markov games(also called stochastic games) lay down the foundation for the study of multi-agent reinforcement learning and se quential agent *** introduce approximate Markov perfect equilibrium as a solution to the computational problem of finite-state sto chastic games repeated in the infinite horizon and prove its *** solution concept preserves the Markov perfect property and opens up the possibility for the success of multi-agent reinforcement learning algorithms on static two-player games to be extended to multi-agent dynamic games,expanding the reign of the PPAD-complete class.

关键词： Markov game multi-agent reinforcement learning Markov perfect equilibrium PPAD-completeness stochastic game

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CNN-LSTM: A Novel Hybrid Deep Neural Network Model for Brain Tumor Classification

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Intelligent Automation & Soft computing 2023年第7期37卷 1129-1143页

作者： R.D.Dhaniya K.M.Umamaheswari Department of Computer Science and Engineering SRM Institute of Science and TechnologyKattankulathur603202India Department of Computing technologies SRM Institute of Science and TechnologyKattankulathur603202India

Current revelations in medical imaging have seen a slew of computer-aided diagnostic(CAD)tools for radiologists *** tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging(MRI).In this work,we reported on new observations based on binary brain tumor categorization using HYBRID ***,the collected image is pre-processed and augmented using the following steps such as rotation,cropping,zooming,CLAHE(Contrast Limited Adaptive Histogram Equalization),and Random Rotation with panoramic stitching(RRPS).Then,a method called particle swarm optimization(PSO)is used to segment tumor regions in an MR *** that,a hybrid CNN-LSTM classifier is applied to classify an image as a tumor or *** this proposed hybrid model,the CNN classifier is used for generating the feature map and the LSTM classifier is used for the classification *** effectiveness of the proposed approach is analyzed based on the different metrics and outcomes compared to different methods.

关键词： Brain tumor segmentation particle swarm optimization CNN-LSTM convolution neural network

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SMOTE-Based deep network with adaptive boosted sooty for the detection and classification of type 2 diabetes mellitus

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Multimedia Tools and Applications 2024年 1-26页

作者： Immadisetty, Phani Kumar Rajabhushanam, C. Computer Science Engineering Bharath Institute of Science and Technology BIST Selaiyur Tamil Nadu Chennai600073 India School of Computing Bharath Institute of Science and Technology BIST Selaiyur Tamil Nadu Chennai600073 India

Type 2 diabetes (T2D) is a prolonged disease caused by abnormal rise in glucose levels due to poor insulin production in the pancreas. However, the detection and classification of this type of disease is very challenging and requires effective techniques for learning the T2D features. Therefore, this study proposes the use of a novel hybridized deep learning-based technique to automatically detect and categorize T2D by effectively learning disease attributes. First, missing value imputation and a normalization-based pre-processing phase are introduced to improve the quality of the data. The Adaptive Boosted Sooty Tern Optimization (Adap-BSTO) approach is then used to select the best features while minimizing complexity. After that, the Synthetic Minority Oversampling Technique (SMOTE) is used to verify that the database classes are evenly distributed. Finally, the Deep Convolutional Attention-based Bidirectional Recurrent Neural Network (DCA-BiRNN) technique is proposed to detect and classify the presence and absence of T2D disease accurately. The proposed study is instigated via the Python platform, and two publicly available PIMA Indian and HFD databases are utilized in this study. Accuracy, NPV, kappa score, Mathew's correlation coefficient (MCC), false discovery rate (FDR), and time complexity are among the assessment metrics examined and compared to prior research. For the PIMA Indian dataset, the proposed method obtains an overall accuracy of 99.6%, FDR of 0.0038, kappa of 99.24%, and NPV of 99.6%. For the HFD dataset, the proposed method acquires an overall accuracy of 99.5%, FDR of 0.0052, kappa of 99%, and NPV of 99.4%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Recurrent neural networks

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Committed-programming reductions: formalizations,implications and relations

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science China(Information sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

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Smart Nutrient Deficiency Prediction System for Groundnut Leaf

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Intelligent Automation & Soft computing 2023年第5期36卷 1845-1862页

作者： Janani Malaisamy JebakumarRethnaraj Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengalpattuTamil Nadu603203India

Prediction of the nutrient deﬁciency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in optimum amounts will protect the environment’s condition and human health *** identiﬁcation also prevents the disease’s occurrence in groundnut crops.A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitro-gen nutrient deﬁciency through image *** chlorophyll and nitrogen are proportionate to one another,the Smart Nutrient Deﬁciency Prediction System(SNDP)is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be *** model’sﬁrst part is to per-form preprocessing using Groundnut Leaf Image Preprocessing(GLIP).Then,in the second part,feature extraction using a convolution process with Non-negative ReLU(CNNR)is done,and then,in the third part,the extracted features areﬂat-tened and given to the dense layer(DL)***,the Maximum Margin clas-siﬁer(MMC)is deployed and takes the input from DL for the classiﬁcation process toﬁnd *** dataset used in this work has no visible symptoms of a deﬁciency with three categories:low level(LL),beginning stage of low level(BSLL),and appropriate level(AL).This model could help to predict nitrogen deﬁciency before perceivable *** performance of the implemented model is analysed and compared with ImageNet pre-trained *** result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99%and 95%,respectively,compared to existing pre-trained models.

关键词： CNN chlorophyll groundnut crop deep learning nitrogen deﬁciency

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Leveraging Explainable AI for Improved Understanding and Prediction of Drug Responses in IGF1R Signaling Pathways

Leveraging Explainable AI for Improved Understanding and Pre...

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2024 International Conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Janiel, J. Rajendran, P. Selvi School Of Computing Hindustan Institute Of Technology And Science Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Pharmacogenomics showcases the aim of precision medicine, which strives to customize treatments for individuals and specific populations. This field delves into exploring how an individuals DNA influences their response to medications. A persons genetic composition can impact the likelihood of experiencing reactions or determining the effectiveness of a medication. By providing insights into the safety and effectiveness of drug therapies pharmacogenomics holds potential for significantly enhancing health outcomes. Through advancements in targeted therapies we can precisely target abnormalities that trigger tumor growth in patients. For instance IGF1R (Insulin like Growth Factor 1 Receptor) which belongs to the tyrosine kinase receptor family plays a crucial role in promoting cell growth, survival and proliferation across different types of cancers. The overexpression of IGF1R has been observed in cancer types indicating its involvement in fueling continuous growth and survival of cancer cells. Targeting IGF1R helps address the dysregulation of this receptor within cancer cells. Artificial Intelligence (AI) comes into play by enabling prediction of suitable drugs based on a patients genomic profile thereby reducing adverse effects and improving treatment effectiveness. Parallel, here has been growing concern regarding model explanation due, to the opaque nature of model predictions. This is particularly important when it comes to modeling drug responses. In our research paper we have employed AI to gain a clear understanding of the prediction model and the factors that affect its results. The findings show that lower valued counts of YAP-pS127-Caution protein tend to negatively impact the output. Similarly lower values of YAP-pS127-Caution protein and higher valued counts of YAP-pS127 -Caution protein, Xanthine, Tyrosine tends to positively impact the output. This helps as an aiding reference in knowing which feature of an unknown cell line should be focused to know

关键词： Proteins

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Investigation of Android Malware Using Deep Learning Approach

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Intelligent Automation & Soft computing 2023年第2期35卷 2413-2429页

作者： V.Joseph Raymond R.Jeberson Retna Raj Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119TamilnaduIndia School of Computing SRM Institute of Science and TechnologyChennai603203TamilnaduIndia

In recent days the usage of android smartphones has increased exten-sively by *** are several applications in different categories bank-ing/ﬁnance,social engineering,education,sports andﬁtness,and many more *** android stack is more vulnerable compared to other mobile plat-forms like IOS,Windows,or Blackberry because of the open-source *** the Existing system,malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth *** attackers target the victim with various attacks like adware,backdoor,spyware,ransomware,and zero-day exploits and create threat hunts on the day-to-day *** the existing approach,there are various tradi-tional machine learning classiﬁers for building a decision support system with limitations such as low detection rate and less feature *** important contents taken for building model from android applications like Intent Filter,Per-mission Signature,API Calls,and System commands are taken from the manifestﬁ*** function parameters of various machine and deep learning classiﬁers like Nave Bayes,k-Nearest Neighbors(k-NN),Support Vector Machine(SVM),Ada Boost,and Multi-Layer Perceptron(MLP)are done for effective *** our pro-posed work,we have used an unsupervised learning multilayer perceptron with multiple target labels and built a model with a better accuracy rate compared to logistic regression,and rank the best features for detection of applications and clas-sify as malicious or benign can be used as threat model by online antivirus scanners.

关键词： Android application permissions multilayer perception relief scoring

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A Multi-path Droplet Routing Protocol for Digital Microfluidic Biochip

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Informatica (Slovenia) 2024年第7期48卷 95-102页

作者： Swain, Jyotiranjan Pyne, Sumanta School of Computing and Information Technology REVA University Bangalore India Department of Computer Science and Engineering National Institute of Technology Rourkela India

Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It involves challenges like moving droplets without violating the fluid constraints. This article proposed a multi-path droplet routing protocol. Multiple routes are explored and validated using special packets. These routes are then classified based on user-defined heuristics. A single route is sequentially selected for each droplet. The routes are then compacted to generate a parallel moving sequence. The simulation result shows 2.087% and 4.952% improvement in the latest arrival time for free and virtual topology, respectively. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Biochips

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Adaptive network combination for single-image reflection removal:a domain generalization perspective

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Frontiers of computer science 2025年第1期19卷 171-173页

作者： Ming LIU Jianan PAN Zifei YAN Wangmeng ZUO Lei ZHANG School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China

1 Introduction Recently,multiple synthetic and real-world datasets have been built to facilitate the training of deep single-image reflection removal(SIRR)***,diverse testing sets are also provided with different types of reflections and ***,the non-negligible domain gaps between training and testing sets make it difficult to learn deep models generalizing well to testing *** diversity of reflections and scenes further makes it a mission impossible to learn a single model being effective for all testing sets and real-world *** this paper,we tackle these issues by learning SIRR models from a domain generalization perspective.

关键词： testing generalization while

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Multi-Distributed Speech Emotion Recognition Based on Mel Frequency Cepstogram and Parameter Transfer

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Chinese Journal of Electronics 2022年第1期31卷 155-167页

作者： LIN Long TAN Liang School of Computer Science Sichuan Normal University Institute of Computing Technology Chinese Academy of Sciences

Speech emotion recognition(SER) is the use of speech signals to estimate the state of emotion. At present, machine learning is one of the main research methods of SER, the test and training data S of traditional machine learning all have the same distribution and feature space, but the data of speech is accessed from different environments and devices, with different distribution characteristics in real life. Thus, the traditional machine learning method is applied to the poor performance of SER. This paper proposes a multi-distributed SER method based on Mel frequency cepstogram(MFCC) and parameter transfer. The method is based on single-layer long short-term memory(LSTM), pre-trained inceptionv3 network and multi-distribution corpus. The speech pre-processed MFCC is taken as the input of single-layer LSTM, and input to the pre-trained inception-v3 *** features are extracted through the pre-trained inception-v3 model. Then the features are sent to the newly defined the fully connected layer and classification layer,let the parameters of the fully connected layer be finetuned, finally get the classification result. The experiment proves that the method can effectively complete the classification of multi-distribution speech emotions and is more effective than the traditional machine learning framework of SER.

关键词： multidistribution corpus fully connected layer feature extraction speech signals machine learning method pre-trained inception-v3 model multidistributed speech emotion recognition pre-trained inception-v3 network feature space recurrent neural nets Mel frequency cepstogram parameter transfer single-layer LSTM speech pre-processed MFCC learning (artificial intelligence) speech recognition emotion recognition multidistributed SER method multidistribution speech emotions

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